Where data ends, the real business intelligence begins

Scientific method: SFL Scientific co-founders Michael Segala (left) and Daniel Ferrante believe data is only the beginning of business intelligence.

May 18, 2017

By GREGORY ZELLER //

In the Big Data era, simply having data isn’t enough – it’s what you do with it that counts.

That’s the thinking behind SFL Scientific LLC, a 2015 startup that puts the intelligence in “business intelligence” by not only crunching numbers, but helping clients understand what the algorithms and other data-driven discoveries really mean – and how best to capitalize on them.

Technically headquartered in Boston, but with the bulk of its operations based in Huntington, the industry-agnostic data-science consultancy is very much “a traditional kind of professional-service consulting firm,” according to cofounder and CEO Michael Segala, albeit one that specializes in “machine learning, artificial intelligence and business intelligence, all aimed at creating unique value propositions for the client.”

“The spectrum of services we provide is pretty large,” Segala told Innovate LI. “It can range from holistic data strategies to sitting down with a company’s data-governance board and helping them determine exactly what they’re trying to accomplish, to defining the business side of the data – what does your future look like, centered on your data?”

The future CEO began developing the thinking behind this business model while completing his PhD in particle physics at Brown University. He and fellow particle scientists Daniel Ferrante and Mike Luk would head off in different directions after completing their Ivy League doctorates, but their shared belief in holistic data strategies would soon reunite them as SFL Scientific cofounders, later adding Alexander Tolpygo – who earned bachelor’s degrees in biomedical engineering and applied mathematics at Stony Brook University – as chief operations officer.

“By basically doing data science for physics in the academic environment, we really started acquiring the skills and the knowledge of how to do these things,” Segala recalled. “We each got jobs as data scientists, and then we really saw the need.”

The goal is to tailor custom products around the clients’ unique data needs. And while it’s a little more involved than one-size-fits-all, Segala and his partners – Ferrante is chief data officer, Luk serves as chief technologist – have been careful to develop methods that apply to virtually any industry.

“We’ve noticed that all of the companies we work with are truly agnostic to their industry or business unit,” Segala noted. “It doesn’t matter if they’re in pharma or public relations. What they’re trying to resolve at the data level is very similar.

“So we tend to look at business problems exclusively from a data perspective,” the CEO added. “And that’s how we started identifying the different types of data.”

Those “four main types of data,” from SFL Scientific’s perspective, are “text or natural language processing,” “time-series,” “images and video” and “market information.”

Alexander Tolpygo: Your data’s great, we make it better.

There are always exceptions – “That oddball that doesn’t fall into these categories directly,” Ferrante noted, “like the guy who only wants to look at satellite images” – but according to Segala, “these categories make up probably 90 percent of the problems that need solving in all industries.”

After identifying the category, SFL Scientific gets busy on the best customized solution for the mystery at hand.

“We’re not pushing a particular product or platform,” Tolpygo noted. “The idea is that clients have specific data types they have been collecting, and we provide services that can help make sense of this data.”

And that’s where the intelligence comes in – more than just developing algorithms or recognizing data patterns, but creating actual, actionable strategies based on that information.

“Business value is driven by having the end user consume the algorithms we provide,” Segala said. “If they can’t make them actionable, what we do is irrelevant.

“We need to make sure our offerings provide that business value,” the CEO added. “That’s what we think of as business intelligence – that end thing we put on our product.”

While SFL Scientific’s analytics have generated “nice traction in most main industries,” according to Segala, the startup has noticed particular interest from pharmaceuticals, financial-service firms and healthcare-focused clients. The principals plan to widen that range with new products and services – there’s gold to be mined in cellular-based data, for instance, but they’re otherwise tightlipped about their product-development plans.

“One of the beautiful things about what we do is we get to see all this innovation driven by other startups,” Segala noted. “We get to see what’s in the market today, what’s missing and what will need to be in the market in five or 10 years.

“Then we can invest in putting out software solutions that will fill these gaps.”

The partners also plan to grow SFL Scientific itself. Currently boasting eight full-time employees, including the four-man executive branch, the goal is to “double in size by the end of the year,” according to Tolpygo, with expanded sales and marketing staffers and an influx of additional data scientists, spread across the Boston and Huntington offices.

For the immediate future, the mission is to continue solving those data-based mysteries in ways their competitors can’t conceive of, according to Segala, who referenced two primary types of opposition.

The first are “off-the-shelf” products that purport to solve data problems, he noted, by “shoving your data into a box.”

“People who use these products don’t get customized solutions,” he said. “They don’t get something that’s meaningful for them. They don’t get solutions that drive business decisions or push anything forward.”

The other main competitor is the older management-consulting firm with the “startup data department,” the CEO added.

“But again, they focus on solving these low-hanging-fruit-type of problems,” Segala said. “We focus more on the core challenges in data science.

“What we want to help clients understand is, what do you do with all that output?”